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Data for: Recent range shifts of moths, butterflies, and birds are driven by the breadth of their climatic niche


Hällfors, Maria H. et al. (2023), Data for: Recent range shifts of moths, butterflies, and birds are driven by the breadth of their climatic niche, Dryad, Dataset,


Species are altering their ranges as a response to climate change, but the magnitude and direction of observed range shifts vary considerably among species. The ability to persist in current areas and colonize new areas plays a crucial role in determining which species will thrive and which decline as climate change progresses. Several studies have sought to identify characteristics, such as morphological and life-history traits, that could explain differences in the capability of species to shift their ranges together with a changing climate. These characteristics have explained variation in range shifts only sporadically, thus offering an uncertain tool for discerning responses among species. As long-term selection to past climates have shaped species’ tolerances, metrics describing species’ contemporary climatic niches may provide an alternative means for understanding responses to on-going climate change. Species that occur in a broader range of climatic conditions may hold greater tolerance to climatic variability and could therefore more readily maintain their historical ranges, while species with more narrow tolerances may only persist if they are able to shift in space to track their climatic niche. Here, we provide a first-filter test of the effect of climatic niche dimensions on shifts in the leading range edges in three relatively well-dispersing species groups. Based on the realized changes in the northern range edges of 383 moth, butterfly, and bird species across a boreal 1100 km latitudinal gradient over c. 20 years, we show that while most morphological or life-history traits were not strongly connected with range shifts, moths and birds occupying a narrower thermal niche and butterflies occupying a broader moisture niche across their European distribution show stronger shifts towards the north. Our results indicate that the climatic niche may be important for predicting responses under climate change and as such warrants further investigation of potential mechanistic underpinnings. 


Distribution data

For Lepidoptera, we started off with a selection of 244 moth and 91 butterfly species. These moths and 45 of the butterfly species were selected for a previous study on phenology and range shifts based on the availability of adequate numbers of systematically collected monitoring and trap dates (Hällfors et al., 2021). Although we do not use those moth trap data in this study, we opted for using the same moth species here as in Hällfors et al. (2021) allowing direct comparisons between the studies, and as this set covers the most common and abundantly occurring species in Finland. The lepidopteran species in this study cover almost 80% of the butterfly species ever observed in Finland and circa 27% of moths commonly monitored in Finland. We excluded five butterfly species and one moth species that are migratory and do not have a permanent breeding population in Finland (Pieris rapae, P. brassicae, Vanessa atalanta, V. cardui, Colias hyale, and Autographa gamma), leaving us with 243 moth and 86 butterfly species at this stage. For these species, we sourced observations that were available in the Insect database and National Butterfly Monitoring Scheme (NAFI; Saarinen et al., 2003), through the Finnish Biodiversity Information Facility (FinBIF) in December 2019 (moths) and June 2022 (butterflies). For moths, the data sourcing from FinBIF was conducted in December 2019 on the superfamily or species level and the following batches were downloaded:, 38387, 38386, 38404. 1,858,745 observations on the 244 moth species were thereafter separated from the other species within the R environment. For butterflies, data on the 91 species was directly sourced into R using the FinBIF R package (Morris 2020) in June 2022, yielding a total of 768 027 observations.

The data on lepidoptera were divided into two five-year periods: 1992–1996 (hereafter T1) and 2013–2017 (hereafter T2) and converted into presence-only data for each 10 x 10 km grid square (119,621 observations). We excluded two moth and 23 butterfly species that had been observed in less than 20 grid cells in either one of the time periods (117,486 observations). The total number of presence squares at T2 was substantially higher than at T1 due to increased sampling effort over time. To account for the change in sampling effort, we divided the data into five latitudinal zones and randomly subsampled the pooled observations of all species in T2 so that the number of observations in T2 matched the number of observations in T1 within the latitudinal zone. This was repeated five times leaving us with five subsets with a total of 78,380 observations in each for these 241 moth and 63 butterfly species.

For birds, we used distribution data on terrestrial breeding birds, sourced from three national bird atlases through the Finnish Natural History Museum (Results of the 1st, 2nd and 3rd Finnish bird atlas. Finnish Museum of Natural History, University of Helsinki (Luomus). Used with Creative Commons Attribution 4.0 -license. These atlases have been compiled from national bird surveys carried out during 1974–1979, 1986–1989 and 2006–2010, respectively. The three atlases contain an index of breeding probability (ranging from 0 = not found; to 4 = confirmed breeding) for each bird species on a 10 x 10 km uniform grid that covers the entire area of Finland (3813 grid squares; Väisänen et al., 1998). We took breeding probability of 0 (not found) to represent absence and all other classes to represent presences. As the third atlas has been surveyed more extensively in comparison to first two atlases, and following earlier practices, we used the pooled first and second atlas data, covering the time period 1974–1989 (hereafter T1) and compared this to the third (2006–2010) atlas (hereafter T2) to reduce potential observation biases due to differences in survey effort (Kujala et al. 2013; Virkkala & Lehikoinen, 2017). We excluded all waterfowl and species that had been observed breeding in less than 20 grid cells in either one of the time periods leaving us with 177 bird species.

Finally, we harmonized the distribution extents of the study species and included only species that likely have their northern range borders in Finland. In Finland, moths, butterflies, and breeding birds include several arctic and subarctic species that predominantly occupy the northern parts of the country and for which the leading distribution edge is close to or outside the country borders. Consequently, we removed two moth, six butterfly and 90 bird species for which the centre point of distribution in Finland in T1 was ≥ 7 000 000 north in the Finnish uniform coordination system (63°4' N in degrees; range in Finland 59°46' – 70°5'N) (Brommer, 2004; Brommer et al., 2012; Kujala et al., 2013). For birds, the centre point was weighted by their breeding category in T1. This allowed us to focus on the predominantly southern species with leading distribution edges in Finland. The consequent data used for the main analyses thus consisted of 383 species: 239 species of moths, 57 species of butterflies, and 87 species of birds for which the shift in the northern facets of distribution between two periods of time 17 years apart were measured. After data delimitation the data available for the main analyses represented 383 species: 239 species of moths, 57 species of butterflies (lepidopteradata_Hällfors.csv), and 87 species of birds (birddata_Hällfors.csv) for which the shift in the northern facets of distribution between two periods of time, roughly 20 years apart, were measured.

Climatic niche metrics

We quantified climatic niche metrics following the approach by Schweiger et al. (2014), from where climatic niche metrics were readily available for butterflies. For birds, we used distribution data from BirdLife International (BirdLife International, 2020). These data are based on a variety of sources and give different levels of site occupancy. We included the location where each species is extant or probably extant, native or reintroduced, and known or thought very likely to be resident or occur during the breeding season. For moths we used available digitized atlas information on geometric moths from Heidrich et al., (2018) which are based on The Geometric Moths of Europe volumes 1–4 (Hausmann, 2001, 2004; Mironov, 2003; Hausmann & Viidalepp, 2012; Hausmann et al., 2012.) and digitized atlas maps on 176 other species based on printed atlases (Fibiger 1990, 1993, 1997, 2009, Fibiger et al. 1995, 2007, 2010; de Freina & Witt, 1987, 1990; Gouter et al. 2003; Hacker et al. 2002; Müller et al. 2019; Ronkay et al 1994, 2001; Skou & Sihvonen, 2015; Zilli et al. 2005). The atlas data were overlaid on the CGRS grid (Common European Chorological Grid Reference System from the European Environment Agency). We used interpolated climate data on the same CGRS grid (originally developed in the ALARM project (Settele et al., 2005; Fronzek et al., 2012) and parameters summarized by Schweiger et al. (2014)) to calculate climatic niche metrics for each species. We also derived a metric of range size using this approach (number of occupied grid cells in Europe).  

In this study, we chose the mean and standard deviation (SD) for mean annual temperature (MAT; corresponding to STI, Devictor et al., 2012) and growing degree days above 5°C for January-August (hereafter GDD5) as candidate variables to describe the thermal niche. We chose the mean and standard deviation for annual precipitation sum (PREC) and soil water content of the upper horizon (0.5 m) (SWC) as candidate variables to describe the moisture niche. The mean of each parameter thus describes the average conditions in which the species occurs, while the standard deviation (SD) describes the absolute breadth of the niche. To account for relative variation in the climatic parameter compared to the mean, we also calculated the coefficient of variation (CV=SD/mean). This is meaningful since a large variation around a small mean tends to imply a more variable set of de facto conditions, since variable conditions closer to freezing temperatures or dry conditions can be more physiologically demanding. For calculating CV for MAT, the degrees Celsius were converted into Kelvin. The climatic niche metrics are available in the file Shift_NicheMetrics_Traits.csv.

Data on ecological traits and habitat use

The trait variables used here included: body size (continuous variable), overwintering mode (for birds: Resident; Short-distance migrant, and Long-distance migrant; for Lepidoptera: Adult, Larvae, Pupa, Egg), and number of generations or broods per season (two levels: one or less and two or more). These traits were chosen as they have been linked to species’ responses to climate and environmental change in previous empirical studies (Pöyry et al., 2009; Laaksonen & Lehikoinen, 2013; WallisDeVries, 2014; Lehikoinen & Virkkala, 2017; Välimäki et al., 2016; Kluen et al., 2017; Fourcade et al., 2021). Moreover, these traits are comparable for Lepidoptera and birds, and information about them is available for most of our studied species. We also tested for effect of range size across Europe.

Trait data on birds were based on Solonen (1985) and Cramp et al. (1994) while data on Lepidoptera were collated from several sources (Mikkola & Jalas, 1977, 1979; Mikkola et al., 1985, 1989; Marttila et al., 1991, 1996; Jalas, 1992; Silvonen et al., 2014; Middleton-Weilling et al. 2020). Size was measured as the total wingspan (in mm) of females for moths and butterflies, as wing index for butterflies, and as mean mass for birds. Bird species that are multi-brooded in southern Finland and single-brooded in northern Finland were categorized as having two or more broods. Voltinism in Lepidoptera were combined into two levels: Semi-and univoltine species = one or less, and multivoltine species (including bivoltine species) = two or more (Pöyry et al., 2017). The trait values per species are available in the file Shift_NicheMetrics_Traits.csv.


Estimating shift in range boundaries

To estimate the magnitude and direction (southward or northward) of the northern range boundary shift between the two time periods, we used quantile regression (Koenker et al., 2017) as implemented in the quantreg package for R (Koenker, 2021). This regression method estimates conditional quantiles of a data distribution instead of a mean outcome. For butterflies and moths, quantile regression was fitted separately for each of the five subsets of the data, after which the estimates for the five outcomes, including 95% confidence intervals, were averaged to produce the final estimate. We estimated the effect that time period (a factor with two levels) had on the 0.75 and 0.9 quantiles of latitudes of distribution points for each species. The models were fitted separately for each species. We inferred the location of the 0.75 and 0.9 perimeter in T1 as the estimated intercept. The location of corresponding perimeters in T2 was arrived at by adding the estimated difference between the two levels of the time period categorical variable to the intercept.  The range shift estimates per species are available in the file Shift_NicheMetrics_Traits.csv.


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